
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE				:Optional[Any]									=							'\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

SCREAMING_SNAKE_CASE				:Tuple									=							'\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

SCREAMING_SNAKE_CASE				:Tuple									=							'\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'


def 		UpperCAmelCase     (      a_						,  a_		)						->      str:







				"""simple docstring"""



				return float((preds == labels).mean()		)


def 		UpperCAmelCase     (      a_						,  a_		)						->      str:







				"""simple docstring"""



				__A       =					simple_accuracy(a_						,  a_		)
				__A       =					float(fa_score(y_true=a_						,  y_pred=a_		)		)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}


def 		UpperCAmelCase     (      a_						,  a_		)						->      List[Any]:







				"""simple docstring"""



				__A       =					float(pearsonr(a_						,  a_		)[0]		)
				__A       =					float(spearmanr(a_						,  a_		)[0]		)
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION    )
class 				UpperCAmelCase (    datasets.Metric    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					str					):
					if self.config_name not in [
					    "sst2",
					    "mnli",
					    "mnli_mismatched",
					    "mnli_matched",
					    "cola",
					    "stsb",
					    "mrpc",
					    "qqp",
					    "qnli",
					    "rte",
					    "wnli",
					    "hans",
					]:
									raise KeyError(
									    "You should supply a configuration name selected in "
									    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
									    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"					)
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,citation=_CITATION				,inputs_description=_KWARGS_DESCRIPTION				,features=datasets.Features(
					        {
					            "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"					),
					            "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"					),
					        }					)				,codebase_urls=[]				,reference_urls=[]				,format="numpy"				,)







	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					List[str]				,A   :					Dict					):
					if self.config_name == "cola":
									return {"matthews_correlation": matthews_corrcoef(A				,A					)}
					elif self.config_name == "stsb":
									return pearson_and_spearman(A				,A					)
					elif self.config_name in ["mrpc", "qqp"]:
									return acc_and_fa(A				,A					)
					elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
									return {"accuracy": simple_accuracy(A				,A					)}
					else:
									raise KeyError(
									    "You should supply a configuration name selected in "
									    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
									    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"					)








"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						int = 10						,							_lowerCamelCase			:						int = 1000						,							_lowerCamelCase			:						bool = True					) ->							int:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), "Invalid type of value(s) specified to function!"

  if min_val > max_val:
    raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'					)
  return min_val if option else max_val







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							int:
  return int((number_a + number_a) / 2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							None:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), 'argument values must be type of "int"'

  if lower > higher:
    raise ValueError('argument value for lower and higher must be(lower > higher)'					)

  if not lower < to_guess < higher:
    raise ValueError(
        'guess value must be within the range of lower and higher value'					)

  def answer(_lowerCamelCase			:						int					) -> str:
    if number > to_guess:
      return "high"
    elif number < to_guess:
      return "low"
    else:
      return "same"

  print('started...'					)

  lowerCamelCase_       	=		lower
  lowerCamelCase_       	=		higher

  lowerCamelCase_       	=		[]

  while True:
    lowerCamelCase_       	=		get_avg(_lowerCamelCase						,							_lowerCamelCase					)
    last_numbers.append(_lowerCamelCase					)

    if answer(_lowerCamelCase					) == "low":
      lowerCamelCase_       	=		number
    elif answer(_lowerCamelCase					) == "high":
      lowerCamelCase_       	=		number
    else:
      break

  print(F'''guess the number : {last_numbers[-1]}'''					)
  print(F'''details : {last_numbers!s}'''					)







def 	lowerCamelCase__			(				) ->							None:
  lowerCamelCase_       	=		int(input('Enter lower value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter high value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter value to guess : '					).strip()					)
  guess_the_number(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)


if __name__ == "__main__":
  main()




